Sync local Space with Hub

prediction_helper.py

@@ -0,0 +1,31 @@
+import pickle
+import numpy as np
+from sklearn import datasets
+import pandas as pd
+
+iris_k_mean_model=pickle.load(open('model.sav', 'rb'))
+classes=['versicolor', 'setosa' , 'virginica']
+
+iris = datasets.load_iris()
+x = pd.DataFrame(iris.data, columns=['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width'])
+
+
+def predict_class_way1(new_data_point):
+    # Calculate the Euclidean distances between the new data point and each of the training data points.
+    distances = np.linalg.norm(x - new_data_point, axis=1)
+    # print(distances,len(distances),np.argmin(distances))
+
+    # The data point with the minimum Euclidean distance is the class of the new data point.
+    class_label = classes[iris_k_mean_model.labels_[np.argmin(distances)]]
+
+    return class_label
+
+def predict_class_way2(new_data_point):
+    # Calculate the distances between the new data point and each of the cluster centers.
+    distances = np.linalg.norm(iris_k_mean_model.cluster_centers_ - new_data_point, axis=1)
+    # print(distances,len(distances),np.argmin(distances))
+
+    # The data point with the minimum Euclidean distance is the class of the new data point.
+    class_label = classes[np.argmin(distances)]
+
+    return class_label